Medical imaging is an important technology used to gain anatomic and physiologic data about a patient's body, organs, tissues, or a portion thereof for clinical diagnosis and treatment planning. Medical imaging includes, but is not limited to, radiography, computed tomography (CT), magnetic resonance imaging (MRI), fluoroscopy, single-photon emission computed tomography (SPECT), positron emission tomography (PET), scintigraphy, ultrasound, and specific techniques such as echocardiography, mammography, intravascular ultrasound, and angiography. Imaging data may be obtained through non-invasive or invasive procedures. The fields of cardiology, neuroscience, oncology, orthopedics, and many others benefit from information obtained in medical imaging.
In the field of cardiology, in particular, it is well known that coronary artery disease may cause the blood vessels providing blood to the heart to develop lesions, such as a stenosis (abnormal narrowing of a blood vessel). As a result, blood flow to the heart may be restricted. A patient suffering from coronary artery disease may experience chest pain, referred to as chronic stable angina during physical exertion or unstable angina when the patient is at rest. A more severe manifestation of disease may lead to myocardial infarction, or heart attack. A need exists to provide more accurate data relating to coronary lesions, e.g., size, shape, location, functional significance (e.g., whether the lesion impacts blood flow), etc. Patients suffering from chest pain and/or exhibiting symptoms of coronary artery disease may be subjected to one or more tests, such as based on medical imaging, that may provide some indirect evidence relating to coronary lesions.
In addition to CT, SPECT, and PT, the use of medical imaging for noninvasive coronary evaluation may include electrocardiograms, biomarker evaluation from blood tests, treadmill tests, and echocardiography. These noninvasive tests, however, typically do not provide a direct assessment of coronary lesions or assess blood flow rates. The noninvasive tests may provide indirect evidence of coronary lesions by looking for changes in electrical activity of the heart (e.g., using electrocardiography (ECG)), motion of the myocardium (e.g., using stress echocardiography), perfusion of the myocardium (e.g., using PET or SPECT), or metabolic changes (e.g., using biomarkers).
For example, anatomic data may be obtained noninvasively using coronary computed tomographic angiography (CCTA). CCTA may be used for imaging of patients with chest pain and involves using CT technology to image the heart and the coronary arteries following an intravenous infusion of a contrast agent. However, CCTA also cannot provide direct information on the functional significance of coronary lesions, e.g., whether the lesions affect blood flow. In addition, since CCTA is purely a diagnostic test, it can neither be used to predict changes in coronary blood flow, pressure, or myocardial perfusion under other physiologic states (e.g., exercise), nor can it be used to predict outcomes of interventions.
Thus, patients may require an invasive test, such as diagnostic cardiac catheterization, to visualize coronary lesions. Diagnostic cardiac catheterization may include performing conventional coronary angiography (CCA) to gather anatomic data on coronary lesions by providing a doctor with an image of the size and shape of the arteries. CCA, however, does not provide data for assessing the functional significance of coronary lesions. For example, a doctor may not be able to diagnose whether a coronary lesion is harmful without determining whether the lesion is functionally significant. Thus, CCA has led to a procedure referred to as an “oculostenotic reflex,” in which interventional cardiologists insert a stent for every lesion found with CCA regardless of whether the lesion is functionally significant. As a result, CCA may lead to unnecessary operations on the patient, which may pose added risks to patients and may result in unnecessary heath care costs for patients.
During diagnostic cardiac catheterization, the functional significance of a coronary lesion may be assessed invasively by measuring the fractional flow reserve (FFR) of an observed lesion. FFR is defined as the ratio of the mean blood pressure downstream of a lesion divided by the mean blood pressure upstream from the lesion, e.g., the aortic pressure, under conditions of increased coronary blood flow, e.g., when induced by intravenous administration of adenosine. Blood pressures may be measured by inserting a pressure wire into the patient. Thus, the decision to treat a lesion based on the determined FFR may be made after the initial cost and risk of diagnostic cardiac catheterization has already been incurred.
To fill the gaps left by each of the pure medical imaging and invasive procedures described above, HeartFlow, Inc. has developed simulation and modeling technology based on patient-specific imaging data. For example, various simulation, modeling, and computational techniques include, but are not limited to: computational mechanics, computational fluid dynamics (CFD), numerical simulation, multi-scale modeling, monte carlo simulation, machine learning, artificial intelligence and various other computational methods to solve mathematical models. These techniques may provide information about biomechanics, fluid mechanics, changes to anatomy and physiology over time, electrophysiology, stresses and strains on tissue, organ function, and neurologic function, among others. This information may be provided at the time of the imaging study or prediction of changes over time as a result of medical procedures or the passage of time and progression of disease.
One illustrative application of computational simulation and modeling is described by HeartFlow, Inc., for modeling vascular blood flow from non-invasive imaging data, including assessing the effect of various medical, interventional, or surgical treatments (see, e.g., U.S. Pat. Nos. 8,386,188; 8,321,150; 8,315,814; 8,315,813; 8,315,812; 8,311,750; 8,311,748; 8,311,747; and 8,157,742). In particular, HeartFlow, Inc. has developed methods for assessing coronary anatomy, myocardial perfusion, and coronary artery flow, noninvasively, to reduce the above disadvantages of invasive FFR measurements. Specifically, CFD simulations have been successfully used to predict spatial and temporal variations of flow rate and pressure of blood in arteries, including FFR. Such methods and systems benefit cardiologists who diagnose and plan treatments for patients with suspected coronary artery disease, and predict coronary artery flow and myocardial perfusion under conditions that cannot be directly measured, e.g., exercise, and to predict outcomes of medical, interventional, and surgical treatments on coronary artery blood flow and myocardial perfusion.
For the above-described techniques, and many other applications of image-based modeling and simulation, the characteristics and quality of the image data is important. During acquisition of medical imaging data, a variety of artifacts or limitations may exist that affect the quality of the image. For example, settings and capabilities of spatial and temporal resolution, energy-tissue interactions, patient or organ movement, reconstruction algorithms, hardware failures, timing or acquisition, detector sensitivity, medication or contrast media administered, patient preparation, and various other factors can affect the resulting image quality. Effects include, but are not limited to, poor resolution, motion or blurring artifacts, high noise, low contrast of tissue, poor perfusion, partial volume effect, distortion, clipping of structures, shadowing, etc. Since these quality issues may affect the performance and accuracy of models and simulations based on the imaging data, there is a need to determine if image quality is suitable or to determine the effect of image quality on modeling and simulation results.
As a result, there is a need for methods and systems for assessing and quantifying medical image quality and, more particularly, to methods and systems for assessing and quantifying medical image quality in relation to patient-specific modeling of blood flow. The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.